Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with Neural Networks

arXiv:2403.01776v1 Announce Type: cross
Abstract: An extendable, efficient and explainable Machine Learning approach is proposed to represent cyclic plasticity and replace conventional material models based on the Radial Return Mapping algorithm. High accuracy and stability by means of a limited amount of training data is achieved by implementing physics-informed regularizations and the back stress information. The off-loading of the Neural Network is applied to the maximal extent. The proposed model architecture is simpler and more efficient compared to existing solutions …

abstract accuracy algorithm arxiv cond-mat.mtrl-sci cs.lg data data-driven explainable machine learning hybrid machine machine learning mapping material networks neural networks nlin.ao physics physics.comp-ph physics-informed stability training training data type